code: def _parallel_fit(
      X,
      y,
      window_size,
      word_lengths,
      alphabet_size,
      norm_options,
      anova,
      binning_strategy,
      feature_selection,
      bigrams,
      n_jobs,
  ):
      rng = check_random_state(window_size)
      transformer = SFAFast(
          word_length=rng.choice(word_lengths),
          alphabet_size=alphabet_size,
          window_size=window_size,
          norm=rng.choice(norm_options),
          anova=anova,
          binning_method=binning_strategy,
          bigrams=bigrams,
          feature_selection=feature_selection,
          remove_repeat_words=False,
          save_words=False,
          n_jobs=n_jobs,
      )
  
      all_words = transformer.fit_transform(X, y)
      return all_words, transformer
  
file_path: sktime/sktime/classification/dictionary_based/_weasel.py
imports: import math
  import numpy
  from joblib import Parallel, delayed
  from scipy.sparse import hstack
  from sklearn.linear_model import LogisticRegression, RidgeClassifierCV
  from sklearn.utils import check_random_state
  from sktime.classification.base import BaseClassifier
  from sktime.transformations.panel.dictionary_based import SFAFast
label_desc: Use image node_id 1 for calling a global function with example usage: _parallel_fit(X, y, window_size, word_lengths, alphabet_size, norm_options, anova, binning_strategy, feature_selection, bigrams, n_jobs) and returns: all_words,  transformer